BAYESIAN HIERARCHICAL MODELS FOR EXTREME EVENT ATTRIBUTION
|
|
- Bryce Singleton
- 5 years ago
- Views:
Transcription
1 BAYESIAN HIERARCHICAL MODELS FOR EXTREME EVENT ATTRIBUTION Richard L Smith University of North Carolina and SAMSI (Joint with Michael Wehner, Lawrence Berkeley Lab) IDAG Meeting Boulder, February 1-3,
2 SAMSI Program on Mathematical and Statistical Methods for Climate and the Earth System Program Leaders Amy Braverman, Jet Propulsion Laboratory Peter Challenor, Exeter Doug Nychka, National Center for Atmospheric Research Michael Wehner, Lawrence Berkeley National Lab Mary Lou Zeeman, Bowdoin College, Mathematics, Directorate Liaison Richard Smith, SAMSI Local Scientific Coordinators Montse Fuentes, North Carolina State University Christopher Jones, University of North Carolina at Chapel Hill National Advisory Committee Liaisons Habib Najm, Sandia National Laboratory Michael Stein, University of Chicago 2
3 Activities Opening Workshop August 2017, at SAMSI (Research Triangle Park, North Carolina) Closing (Transition) Workshop May 2018, at SAMSI Visitors in residence for all or part of program Postdoc opportunities Additional workshops Possible venues LBNL, NCAR, CICS (at NCDC), possible joint workshop with Newton Institute Current proposed topics for working groups: Reconstructing climate databases using remote sensing data; Global carbon cycle; Parameter estimation in climate models; Data assimilation; Applications of data analytics to climate science; Climate prediction; Climate extremes; Stochastic parameterizations; Climate and health; Applications of dynamical systems and agent-based models to food systems. 3
4 How To Get Involved Workshops by invitation or open registration For most participants, SAMSI will pay travel and hotel costs Registration via Visitor positions open for application Travel and accommodation expenses For some participants, partial salary support Postdoc applications: closing date in December 2016 Please encourage qualified applicants to apply! Application details on Working group participation is open to all Webex 4
5 Objectives of This Talk Two problems How to attribute extreme events to anthropogenic or natural causes (Fraction of Attributable Risk; Stott, Stone, Allen 2004 and much since)) Projection of future extreme events (Christidis, Jones, Stott 2014) The specific idea: develop a method that can be applied to archived climate model runs, without the need for intensive model runs tailored to a specific event A caveat: the results are only as good as the models that generate them absence of evidence (of an anthropogenic effect) is not evidence of absence 5
6 Data Observational data from CRU TS 3.22 land surface monthly average temperatures ( o C) on 0.5 o x0.5 o grid boxes, aggregated to JJA over Europe: spatial averages over 10 o W-40 o E, 30 o N-50 o N Russia: spatial averages over 30 o E-60 o E, 45 o N-65 o N Central USA: spatial averages over 90 o W-105 o W, 25 o N-45 o N Climate model data from CMIP5 40 climate models 142 total runs historical 60 total runs historical natural 74 total runs rcp8.5 Same spatial regions as observational data 6
7 Observational Data 7
8 Approach for a Single Series 8
9 Model Selection 9
10 Observational Data with Fitted 80 th Percentile for Each Year (four curves represent 70%, 75%, 80%, 85% thresholds) 10
11 Posterior Densities for Exceedance Probabilities Compute posterior density for the Binary LOg Threshold Exceedance Probability of the extreme value of interest in each of 4 years (80% threshold) 11
12 Comparing Different Thresholds for the Probability of High Exceedance in
13 Comparing Observations and Models I: All-forcings models (Europe) 13
14 Comparing Observations and Models II: Natural-forcings models (Europe) 14
15 Summary So Far Bayesian approach to extreme value modeling allows us to construct posterior distributions for a high-threshold exceedance based on observational data Clear differences in 2013 against any of 1940, 1960, 1980 but cannot attribute this to human influence Comparison of observational and model data suggests a population of models approach naturally leads into hierarchical modeling 15
16 Outline of Hierarchical Model Θ j : parameter vector for model j, j=0,,n M where N M is the number of models (j=0: observational data) Prior distribution: the Θ j s are IID from a multivariate normal distribution with mean M and precision matrix D (extension: ψd for j=0, possibly ψ 1) Fit by Markov Chain Monte Carlo (MCMC) Do this once for anthropogenic forcings, again for natural forcings Extension: extrapolate to future decades under your favorite rcp scenario basis for future projections of extreme event probabilities 16
17 Posterior Densities (Central USA)
18 Posterior Densities (Russia)
19 Posterior Densities (Europe)
20 Quantiles of the Posterior Density of the Binary Log Risk Ratio (BLORRAT)
21 Changes in Projected Extreme Event Probabilities Over Time 21
22 Future Work This is still very much a vanilla MCMC algorithm: apart from needing more tuning, possibilities for treating K or the threshold as unknowns, alternatives to multivariate normal (e.g. Dirichlet process) for prior distributions Bivariate extensions, e.g. joint distributions of temperature and precipitation Fully spatial model possibility of hierarchical models based on max-stable processes (need a full likelihood problematic but see Wadsworth and Tawn (2014), Thibaud et al. (2016, in review) for recent developments) 22
INFLUENCE OF CLIMATE CHANGE ON EXTREME WEATHER EVENTS
INFLUENCE OF CLIMATE CHANGE ON EXTREME WEATHER EVENTS Richard L Smith University of North Carolina and SAMSI (Joint with Michael Wehner, Lawrence Berkeley Lab) VI-MSS Workshop on Environmental Statistics
More informationHIERARCHICAL MODELS IN EXTREME VALUE THEORY
HIERARCHICAL MODELS IN EXTREME VALUE THEORY Richard L. Smith Department of Statistics and Operations Research, University of North Carolina, Chapel Hill and Statistical and Applied Mathematical Sciences
More informationData. Climate model data from CMIP3
Data Observational data from CRU (Climate Research Unit, University of East Anglia, UK) monthly averages on 5 o x5 o grid boxes, aggregated to JJA average anomalies over Europe: spatial averages over 10
More informationA STATISTICAL APPROACH TO OPERATIONAL ATTRIBUTION
A STATISTICAL APPROACH TO OPERATIONAL ATTRIBUTION Richard L. Smith Department of Statistics and Operations Research University of North Carolina Chapel Hill, NC 27599-3260, USA rls@email.unc.edu IDAG Meeting
More informationA HIERARCHICAL MODEL FOR REGRESSION-BASED CLIMATE CHANGE DETECTION AND ATTRIBUTION
A HIERARCHICAL MODEL FOR REGRESSION-BASED CLIMATE CHANGE DETECTION AND ATTRIBUTION Richard L Smith University of North Carolina and SAMSI Joint Statistical Meetings, Montreal, August 7, 2013 www.unc.edu/~rls
More informationRichard L. Smith Department of Statistics and Operations Research University of North Carolina Chapel Hill, NC
EXTREME VALUE THEORY Richard L. Smith Department of Statistics and Operations Research University of North Carolina Chapel Hill, NC 27599-3260 rls@email.unc.edu AMS Committee on Probability and Statistics
More informationClimate Change: the Uncertainty of Certainty
Climate Change: the Uncertainty of Certainty Reinhard Furrer, UZH JSS, Geneva Oct. 30, 2009 Collaboration with: Stephan Sain - NCAR Reto Knutti - ETHZ Claudia Tebaldi - Climate Central Ryan Ford, Doug
More informationProbabilistic assessment of regional climate change: a Bayesian approach to combining. predictions from multi-model ensembles
Probabilistic assessment of regional climate change: a Bayesian approach to combining predictions from multi-model ensembles Claudia Tebaldi (ESIG/NCAR), Richard L. Smith (UNC-Chapel Hill) Doug Nychka
More informationRISK AND EXTREMES: ASSESSING THE PROBABILITIES OF VERY RARE EVENTS
RISK AND EXTREMES: ASSESSING THE PROBABILITIES OF VERY RARE EVENTS Richard L. Smith Department of Statistics and Operations Research University of North Carolina Chapel Hill, NC 27599-3260 rls@email.unc.edu
More informationof the 7 stations. In case the number of daily ozone maxima in a month is less than 15, the corresponding monthly mean was not computed, being treated
Spatial Trends and Spatial Extremes in South Korean Ozone Seokhoon Yun University of Suwon, Department of Applied Statistics Suwon, Kyonggi-do 445-74 South Korea syun@mail.suwon.ac.kr Richard L. Smith
More informationStatistical Science: Contributions to the Administration s Research Priority on Climate Change
Statistical Science: Contributions to the Administration s Research Priority on Climate Change April 2014 A White Paper of the American Statistical Association s Advisory Committee for Climate Change Policy
More informationBayesian dynamic modeling for large space-time weather datasets using Gaussian predictive processes
Bayesian dynamic modeling for large space-time weather datasets using Gaussian predictive processes Alan Gelfand 1 and Andrew O. Finley 2 1 Department of Statistical Science, Duke University, Durham, North
More informationBayesian dynamic modeling for large space-time weather datasets using Gaussian predictive processes
Bayesian dynamic modeling for large space-time weather datasets using Gaussian predictive processes Sudipto Banerjee 1 and Andrew O. Finley 2 1 Biostatistics, School of Public Health, University of Minnesota,
More informationUncertainty and regional climate experiments
Uncertainty and regional climate experiments Stephan R. Sain Geophysical Statistics Project Institute for Mathematics Applied to Geosciences National Center for Atmospheric Research Boulder, CO Linda Mearns,
More informationA computationally efficient approach to generate large ensembles of coherent climate data for GCAM
A computationally efficient approach to generate large ensembles of coherent climate data for GCAM GCAM Community Modeling Meeting Joint Global Change Research Institute, College Park MD November 7 th,
More informationBayesian dynamic modeling for large space-time weather datasets using Gaussian predictive processes
Bayesian dynamic modeling for large space-time weather datasets using Gaussian predictive processes Andrew O. Finley Department of Forestry & Department of Geography, Michigan State University, Lansing
More informationCLIMATE EXTREMES AND GLOBAL WARMING: A STATISTICIAN S PERSPECTIVE
CLIMATE EXTREMES AND GLOBAL WARMING: A STATISTICIAN S PERSPECTIVE Richard L. Smith Department of Statistics and Operations Research University of North Carolina, Chapel Hill rls@email.unc.edu Statistics
More informationBayesian dynamic modeling for large space-time weather datasets using Gaussian predictive processes
Bayesian dynamic modeling for large space-time weather datasets using Gaussian predictive processes Andrew O. Finley 1 and Sudipto Banerjee 2 1 Department of Forestry & Department of Geography, Michigan
More informationOperational event attribution
Operational event attribution Peter Stott, NCAR, 26 January, 2009 August 2003 Events July 2007 January 2009 January 2009 Is global warming slowing down? Arctic Sea Ice Climatesafety.org climatesafety.org
More informationHadley Centre for Climate Prediction and Research, Met Office, FitzRoy Road, Exeter, EX1 3PB, UK.
Temperature Extremes, the Past and the Future. S Brown, P Stott, and R Clark Hadley Centre for Climate Prediction and Research, Met Office, FitzRoy Road, Exeter, EX1 3PB, UK. Tel: +44 (0)1392 886471 Fax
More informationNew Classes of Multivariate Survival Functions
Xiao Qin 2 Richard L. Smith 2 Ruoen Ren School of Economics and Management Beihang University Beijing, China 2 Department of Statistics and Operations Research University of North Carolina Chapel Hill,
More informationStatistical analysis of regional climate models. Douglas Nychka, National Center for Atmospheric Research
Statistical analysis of regional climate models. Douglas Nychka, National Center for Atmospheric Research National Science Foundation Olso workshop, February 2010 Outline Regional models and the NARCCAP
More informationRegional probabilities of precipitation change: A Bayesian analysis of multimodel simulations
GEOPHYSICAL RESEARCH LETTERS, VOL. 31, L24213, doi:10.1029/2004gl021276, 2004 Regional probabilities of precipitation change: A Bayesian analysis of multimodel simulations C. Tebaldi, 1 L. O. Mearns, 1
More informationA new Hierarchical Bayes approach to ensemble-variational data assimilation
A new Hierarchical Bayes approach to ensemble-variational data assimilation Michael Tsyrulnikov and Alexander Rakitko HydroMetCenter of Russia College Park, 20 Oct 2014 Michael Tsyrulnikov and Alexander
More informationBayesian spatial hierarchical modeling for temperature extremes
Bayesian spatial hierarchical modeling for temperature extremes Indriati Bisono Dr. Andrew Robinson Dr. Aloke Phatak Mathematics and Statistics Department The University of Melbourne Maths, Informatics
More informationRISK ANALYSIS AND EXTREMES
RISK ANALYSIS AND EXTREMES Richard L. Smith Department of Statistics and Operations Research University of North Carolina Chapel Hill, NC 27599-3260 rls@email.unc.edu Opening Workshop SAMSI program on
More informationSpatial patterns of probabilistic temperature change projections from a multivariate Bayesian analysis
Zurich Open Repository and Archive University of Zurich Main Library Strickhofstrasse 39 CH-8057 Zurich www.zora.uzh.ch Year: 2007 Spatial patterns of probabilistic temperature change projections from
More informationBayesian hierarchical modelling for data assimilation of past observations and numerical model forecasts
Bayesian hierarchical modelling for data assimilation of past observations and numerical model forecasts Stan Yip Exeter Climate Systems, University of Exeter c.y.yip@ex.ac.uk Joint work with Sujit Sahu
More informationStatistics for extreme & sparse data
Statistics for extreme & sparse data University of Bath December 6, 2018 Plan 1 2 3 4 5 6 The Problem Climate Change = Bad! 4 key problems Volcanic eruptions/catastrophic event prediction. Windstorms
More informationThe 21st century decline in damaging European windstorms David Stephenson & Laura Dawkins Exeter Climate Systems
The 21st century decline in damaging European windstorms David Stephenson & Laura Dawkins Exeter Climate Systems Acknowledgements: Julia Lockwood, Paul Maisey 6 th European Windstorm workshop, Reading,
More information18. ATTRIBUTION OF EXTREME RAINFALL IN SOUTHEAST CHINA DURING MAY 2015
18. ATTRIBUTION OF EXTREME RAINFALL IN SOUTHEAST CHINA DURING MAY 2015 Claire Burke, Peter Stott, Ying Sun, and Andrew Ciavarella Anthropogenic climate change increased the probability that a short-duration,
More informationProbabilities for climate projections
Probabilities for climate projections Claudia Tebaldi, Reinhard Furrer Linda Mearns, Doug Nychka National Center for Atmospheric Research Richard Smith - UNC-Chapel Hill Steve Sain - CU-Denver Statistical
More informationModels for models. Douglas Nychka Geophysical Statistics Project National Center for Atmospheric Research
Models for models Douglas Nychka Geophysical Statistics Project National Center for Atmospheric Research Outline Statistical models and tools Spatial fields (Wavelets) Climate regimes (Regression and clustering)
More informationDetection and Attribution of Climate Change. ... in Indices of Extremes?
Detection and Attribution of Climate Change... in Indices of Extremes? Reiner Schnur Max Planck Institute for Meteorology MPI-M Workshop Climate Change Scenarios and Their Use for Impact Studies September
More informationApproximate Bayesian computation for spatial extremes via open-faced sandwich adjustment
Approximate Bayesian computation for spatial extremes via open-faced sandwich adjustment Ben Shaby SAMSI August 3, 2010 Ben Shaby (SAMSI) OFS adjustment August 3, 2010 1 / 29 Outline 1 Introduction 2 Spatial
More informationModeling conditional distributions with mixture models: Applications in finance and financial decision-making
Modeling conditional distributions with mixture models: Applications in finance and financial decision-making John Geweke University of Iowa, USA Journal of Applied Econometrics Invited Lecture Università
More information19. RECORD-BREAKING HEAT IN NORTHWEST CHINA IN JULY 2015: ANALYSIS OF THE SEVERITY AND UNDERLYING CAUSES
19. RECORD-BREAKING HEAT IN NORTHWEST CHINA IN JULY 2015: ANALYSIS OF THE SEVERITY AND UNDERLYING CAUSES Chiyuan Miao, Qiaohong Sun, Dongxian Kong, and Qingyun Duan The record-breaking heat over northwest
More informationSTATISTICS OF CLIMATE EXTREMES// TRENDS IN CLIMATE DATASETS
STATISTICS OF CLIMATE EXTREMES// TRENDS IN CLIMATE DATASETS Richard L Smith Departments of STOR and Biostatistics, University of North Carolina at Chapel Hill and Statistical and Applied Mathematical Sciences
More informationClimate Futures for Eastern Melbourne. Data provided for the Eastern Alliance for Greenhouse Action CSIRO July 2010 (Updated 2013)
Data provided for the Eastern Alliance for Greenhouse Action CSIRO July 2010 (Updated 2013) 2030 A1B Climate Futures for the 5 grid centred on 37.5 S 146.5 E the 2050s Climate Futures for the 5 grid centred
More informationThe North American Regional Climate Change Assessment Program (NARCCAP) Raymond W. Arritt for the NARCCAP Team Iowa State University, Ames, Iowa USA
The North American Regional Climate Change Assessment Program (NARCCAP) Raymond W. Arritt for the NARCCAP Team Iowa State University, Ames, Iowa USA NARCCAP Participants Raymond Arritt, David Flory, William
More informationAnnouncing a Course Sequence: March 9 th - 10 th, March 11 th, and March 12 th - 13 th 2015 Historic Charleston, South Carolina
Announcing a Course Sequence: Introduction to Bayesian Disease Mapping (IBDM) BDM with INLA (BDMI) Advanced Bayesian Disease Mapping (ABDM) March 9 th - 10 th, March 11 th, and March 12 th - 13 th 2015
More informationRisk Estimation and Uncertainty Quantification by Markov Chain Monte Carlo Methods
Risk Estimation and Uncertainty Quantification by Markov Chain Monte Carlo Methods Konstantin Zuev Institute for Risk and Uncertainty University of Liverpool http://www.liv.ac.uk/risk-and-uncertainty/staff/k-zuev/
More informationPrinciples of Bayesian Inference
Principles of Bayesian Inference Sudipto Banerjee 1 and Andrew O. Finley 2 1 Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, U.S.A. 2 Department of Forestry & Department
More informationBayesian Modelling of Extreme Rainfall Data
Bayesian Modelling of Extreme Rainfall Data Elizabeth Smith A thesis submitted for the degree of Doctor of Philosophy at the University of Newcastle upon Tyne September 2005 UNIVERSITY OF NEWCASTLE Bayesian
More informationModeling conditional distributions with mixture models: Theory and Inference
Modeling conditional distributions with mixture models: Theory and Inference John Geweke University of Iowa, USA Journal of Applied Econometrics Invited Lecture Università di Venezia Italia June 2, 2005
More informationGeneralized Exponential Random Graph Models: Inference for Weighted Graphs
Generalized Exponential Random Graph Models: Inference for Weighted Graphs James D. Wilson University of North Carolina at Chapel Hill June 18th, 2015 Political Networks, 2015 James D. Wilson GERGMs for
More informationClimate Modeling in a Changed World
Climate Modeling in a Changed World Lawrence Buja southern@ucar.edu National Center for Atmospheric Research Boulder, Colorado CAM T341- Jim Hack Weather vs Climate Climate Change Boundary Conditions Threats
More informationA spatio-temporal model for extreme precipitation simulated by a climate model
A spatio-temporal model for extreme precipitation simulated by a climate model Jonathan Jalbert Postdoctoral fellow at McGill University, Montréal Anne-Catherine Favre, Claude Bélisle and Jean-François
More informationBayesian inference for multivariate extreme value distributions
Bayesian inference for multivariate extreme value distributions Sebastian Engelke Clément Dombry, Marco Oesting Toronto, Fields Institute, May 4th, 2016 Main motivation For a parametric model Z F θ of
More informationSuriyanarayanan Vaikuntanathan
Assistant Professor University of Chicago Suriyanarayanan Vaikuntanathan Google Scholar citations: http://scholar.google.com/citations?user=qws4178aaaaj Personal Information Address Department of Chemistry
More informationEarly benefits of mitigation in risk of regional climate extremes
In the format provided by the authors and unedited. DOI: 10.1038/NCLIMATE3259 Early benefits of mitigation in risk of regional climate extremes Andrew Ciavarella 1 *, Peter Stott 1,2 and Jason Lowe 1,3
More informationHistorical and Projected National and Regional Climate Trends
Climate Change Trends for Planning at Sand Creek Massacre National Historic Site Prepared by Nicholas Fisichelli, NPS Climate Change Response Program April 18, 2013 Climate change and National Parks Climate
More informationTomoko Matsuo s collaborators
Tomoko Matsuo s collaborators Thermosphere-Ionosphere GCM Modeling A. Richmond (NCAR-HAO) T. Fuller-Rowell and M. Codrescu (NOAA) Polar Ionosphere Data Assimilation A. Richmond, G. Lu, and B. Emery (NCAR-HAO)
More informationHuman influence on terrestrial precipitation trends revealed by dynamical
1 2 3 Supplemental Information for Human influence on terrestrial precipitation trends revealed by dynamical adjustment 4 Ruixia Guo 1,2, Clara Deser 1,*, Laurent Terray 3 and Flavio Lehner 1 5 6 7 1 Climate
More informationOptimization Tools in an Uncertain Environment
Optimization Tools in an Uncertain Environment Michael C. Ferris University of Wisconsin, Madison Uncertainty Workshop, Chicago: July 21, 2008 Michael Ferris (University of Wisconsin) Stochastic optimization
More informationGlobal Climate Models and Extremes
Global Climate Models and Extremes Francis Zwiers 1, Slava Kharin 1, Xuebin Zhang 1, Gabi Hegerl 2 1 Environment Canada, 2 Duke University Photo: F. Zwiers Outline AMIP2 simulations (subtopic 2) IPCC AR4
More informationA Fully Nonparametric Modeling Approach to. BNP Binary Regression
A Fully Nonparametric Modeling Approach to Binary Regression Maria Department of Applied Mathematics and Statistics University of California, Santa Cruz SBIES, April 27-28, 2012 Outline 1 2 3 Simulation
More informationBayesian Modeling of Accelerated Life Tests with Random Effects
Bayesian Modeling of Accelerated Life Tests with Random Effects Ramón V. León Avery J. Ashby Jayanth Thyagarajan Joint Statistical Meeting August, 00 Toronto, Canada Abstract We show how to use Bayesian
More informationBayesian Point Process Modeling for Extreme Value Analysis, with an Application to Systemic Risk Assessment in Correlated Financial Markets
Bayesian Point Process Modeling for Extreme Value Analysis, with an Application to Systemic Risk Assessment in Correlated Financial Markets Athanasios Kottas Department of Applied Mathematics and Statistics,
More informationLarge Scale Bayesian Inference
Large Scale Bayesian I in Cosmology Jens Jasche Garching, 11 September 2012 Introduction Cosmography 3D density and velocity fields Power-spectra, bi-spectra Dark Energy, Dark Matter, Gravity Cosmological
More informationBayesian Hierarchical Model Characterization of Model Error in Ocean Data Assimilation and Forecasts
DISTRIBUTION STATEMENT A: Distribution approved for public release; distribution is unlimited. Bayesian Hierarchical Model Characterization of Model Error in Ocean Data Assimilation and Forecasts Christopher
More informationSpatial Extremes in Atmospheric Problems
Spatial Extremes in Atmospheric Problems Eric Gilleland Research Applications Laboratory (RAL) National Center for Atmospheric Research (NCAR), Boulder, Colorado, U.S.A. http://www.ral.ucar.edu/staff/ericg
More informationExtreme Precipitation: An Application Modeling N-Year Return Levels at the Station Level
Extreme Precipitation: An Application Modeling N-Year Return Levels at the Station Level Presented by: Elizabeth Shamseldin Joint work with: Richard Smith, Doug Nychka, Steve Sain, Dan Cooley Statistics
More informationParameter Estimation. William H. Jefferys University of Texas at Austin Parameter Estimation 7/26/05 1
Parameter Estimation William H. Jefferys University of Texas at Austin bill@bayesrules.net Parameter Estimation 7/26/05 1 Elements of Inference Inference problems contain two indispensable elements: Data
More informationPrinciples of Bayesian Inference
Principles of Bayesian Inference Sudipto Banerjee and Andrew O. Finley 2 Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, U.S.A. 2 Department of Forestry & Department
More informationThe ENSEMBLES Project
The ENSEMBLES Project Providing ensemble-based predictions of climate changes and their impacts by Dr. Chris Hewitt Abstract The main objective of the ENSEMBLES project is to provide probabilistic estimates
More informationReminder of some Markov Chain properties:
Reminder of some Markov Chain properties: 1. a transition from one state to another occurs probabilistically 2. only state that matters is where you currently are (i.e. given present, future is independent
More informationSTATISTICS FOR CLIMATE SCIENCE
STATISTICS FOR CLIMATE SCIENCE Richard L Smith University of North Carolina and SAMSI VI-MSS Workshop on Environmental Statistics Kolkata, March 2-4, 2015 www.unc.edu/~rls/kolkata.html 1 1 In Memoriam
More informationChapter 4 Dynamic Bayesian Networks Fall Jin Gu, Michael Zhang
Chapter 4 Dynamic Bayesian Networks 2016 Fall Jin Gu, Michael Zhang Reviews: BN Representation Basic steps for BN representations Define variables Define the preliminary relations between variables Check
More informationBayesian Estimation with Sparse Grids
Bayesian Estimation with Sparse Grids Kenneth L. Judd and Thomas M. Mertens Institute on Computational Economics August 7, 27 / 48 Outline Introduction 2 Sparse grids Construction Integration with sparse
More informationUniversity of California High-Performance AstroComputing Center JOEL PRIMACK UCSC
University of California High-Performance AstroComputing Center JOEL PRIMACK UCSC http://hipacc.ucsc.edu/ As computing and observational power continue to increase rapidly, the most difficult problems
More informationBayes Estimation in Meta-analysis using a linear model theorem
University of Wollongong Research Online Applied Statistics Education and Research Collaboration (ASEARC) - Conference Papers Faculty of Engineering and Information Sciences 2012 Bayes Estimation in Meta-analysis
More informationPlanetary boundary layer schemes in RegCM: evaluation and impact on the climate change signal over Europe and Mediterranean region
Seventh ICTP Workshop on the Theory and Use of Regional Climate Models Planetary boundary layer schemes in RegCM: evaluation and impact on the climate change signal over Europe and Mediterranean region
More informationModelling Operational Risk Using Bayesian Inference
Pavel V. Shevchenko Modelling Operational Risk Using Bayesian Inference 4y Springer 1 Operational Risk and Basel II 1 1.1 Introduction to Operational Risk 1 1.2 Defining Operational Risk 4 1.3 Basel II
More informationBayesian Estimation of Input Output Tables for Russia
Bayesian Estimation of Input Output Tables for Russia Oleg Lugovoy (EDF, RANE) Andrey Polbin (RANE) Vladimir Potashnikov (RANE) WIOD Conference April 24, 2012 Groningen Outline Motivation Objectives Bayesian
More informationA Complete Spatial Downscaler
A Complete Spatial Downscaler Yen-Ning Huang, Brian J Reich, Montserrat Fuentes 1 Sankar Arumugam 2 1 Department of Statistics, NC State University 2 Department of Civil, Construction, and Environmental
More informationGenerating gridded fields of extreme precipitation for large domains with a Bayesian hierarchical model
Generating gridded fields of extreme precipitation for large domains with a Bayesian hierarchical model Cameron Bracken Department of Civil, Environmental and Architectural Engineering University of Colorado
More informationBayesian Modeling of Conditional Distributions
Bayesian Modeling of Conditional Distributions John Geweke University of Iowa Indiana University Department of Economics February 27, 2007 Outline Motivation Model description Methods of inference Earnings
More informationDownscaled Climate Change Projection for the Department of Energy s Savannah River Site
Downscaled Climate Change Projection for the Department of Energy s Savannah River Site Carolinas Climate Resilience Conference Charlotte, North Carolina: April 29 th, 2014 David Werth Atmospheric Technologies
More informationOn the Causes of and Long Term Changes in Eurasian Heat Waves
On the Causes of and Long Term Changes in Eurasian Heat Waves Siegfried Schubert Hailan Wang*, Randy Koster, Max Suarez NASA/GSFC Global Modeling and Assimilation Office Second Annual Workshop on Understanding
More informationdata: Precipitation Interpolation
Gridding and Uncertainties in gridded data: Precipitation Interpolation Phil Jones, Richard Cornes, Ian Harris and David Lister Climatic Research Unit UEA Norwich, UK Issues to be discussed Alternative
More information7. CMIP5 MODEL-BASED ASSESSMENT OF ANTHROPOGENIC INFLUENCE ON HIGHLY ANOMALOUS ARCTIC WARMTH DURING NOVEMBER DECEMBER 2016
7. CMIP5 MODEL-BASED ASSESSMENT OF ANTHROPOGENIC INFLUENCE ON HIGHLY ANOMALOUS ARCTIC WARMTH DURING NOVEMBER DECEMBER 2016 Jonghun Kam, Thomas R. Knutson, Fanrong Zeng, and Andrew T. Wittenberg According
More informationForward Problems and their Inverse Solutions
Forward Problems and their Inverse Solutions Sarah Zedler 1,2 1 King Abdullah University of Science and Technology 2 University of Texas at Austin February, 2013 Outline 1 Forward Problem Example Weather
More informationProbability and climate change UK/Russia Climate Workshop, October, 2003
Probability and climate change UK/Russia Climate Workshop, October, 2003 Myles Allen Department of Physics University of Oxford myles.allen@physics.ox.ac.uk South Oxford on January 5 th, 2003 Photo courtesy
More informationThe WRF Developmental Testbed Center (DTC)
The WRF Developmental Testbed Center (DTC) Bob Gall A facility where the NWP research and operational communities interact to accelerate testing and evaluation of new models and techniques for research
More informationTimothy Chumley. Employment. Education. Fields of Research Interest. Grants, Fellowships, Awards. Research Articles
Timothy Chumley Department of Mathematics & Statistics Mount Holyoke College South Hadley, MA 01075 Phone: (413) 538-2299 Office: 404b Clapp Lab Email: tchumley@mtholyoke.edu Homepage: http://mtholyoke.edu/
More informationPhysician Performance Assessment / Spatial Inference of Pollutant Concentrations
Physician Performance Assessment / Spatial Inference of Pollutant Concentrations Dawn Woodard Operations Research & Information Engineering Cornell University Johns Hopkins Dept. of Biostatistics, April
More informationNonparametric Bayesian Methods (Gaussian Processes)
[70240413 Statistical Machine Learning, Spring, 2015] Nonparametric Bayesian Methods (Gaussian Processes) Jun Zhu dcszj@mail.tsinghua.edu.cn http://bigml.cs.tsinghua.edu.cn/~jun State Key Lab of Intelligent
More informationMCMC algorithms for fitting Bayesian models
MCMC algorithms for fitting Bayesian models p. 1/1 MCMC algorithms for fitting Bayesian models Sudipto Banerjee sudiptob@biostat.umn.edu University of Minnesota MCMC algorithms for fitting Bayesian models
More informationApproximate Bayesian Computation
Approximate Bayesian Computation Michael Gutmann https://sites.google.com/site/michaelgutmann University of Helsinki and Aalto University 1st December 2015 Content Two parts: 1. The basics of approximate
More information(5) Multi-parameter models - Gibbs sampling. ST440/540: Applied Bayesian Analysis
Summarizing a posterior Given the data and prior the posterior is determined Summarizing the posterior gives parameter estimates, intervals, and hypothesis tests Most of these computations are integrals
More informationFIRST DRAFT. Science underpinning the prediction and attribution of extreme events
FIRST WCRP Grand Challenges Science underpinning the prediction and attribution of extreme events David Karoly, with input from CLIVAR, ETCCDI, GEWEX, WGSIP, WGCM Needs consultation and feedback Introduction
More informationWeather generators for studying climate change
Weather generators for studying climate change Assessing climate impacts Generating Weather (WGEN) Conditional models for precip Douglas Nychka, Sarah Streett Geophysical Statistics Project, National Center
More informationBayesian Areal Wombling for Geographic Boundary Analysis
Bayesian Areal Wombling for Geographic Boundary Analysis Haolan Lu, Haijun Ma, and Bradley P. Carlin haolanl@biostat.umn.edu, haijunma@biostat.umn.edu, and brad@biostat.umn.edu Division of Biostatistics
More informationEUROPE ATTRIBUTION STATEMENT for 2014 TEMPS. 1. UNIVERSITY OF MELBOURNE: European record 2014 temperatures in CMIP5
METHODOLOGY 1. UNIVERSITY OF MELBOURNE: European record 2014 temperatures in CMIP5 Contacts: David Karoly - dkaroly@unimelb.edu.au; Andrew King - andrew.king@unimelb.edu.au; Sophie Lewis - sophie.lewis@anu.edu.au
More informationLecture 2 APPLICATION OF EXREME VALUE THEORY TO CLIMATE CHANGE. Rick Katz
1 Lecture 2 APPLICATION OF EXREME VALUE THEORY TO CLIMATE CHANGE Rick Katz Institute for Study of Society and Environment National Center for Atmospheric Research Boulder, CO USA email: rwk@ucar.edu Home
More informationSecond Announcement. 2 nd Workshop CGMS International Cloud Working Group. 29 October - 2 November 2018, Madison, Wisconsin, USA
Second Announcement 2 nd Workshop CGMS International Cloud Working Group 29 October - 2 November 2018, Madison, Wisconsin, USA Organized by the Space Science and Engineering Center of the University of
More informationModels for Spatial Extremes. Dan Cooley Department of Statistics Colorado State University. Work supported in part by NSF-DMS
Models for Spatial Extremes Dan Cooley Department of Statistics Colorado State University Work supported in part by NSF-DMS 0905315 Outline 1. Introduction Statistics for Extremes Climate and Weather 2.
More informationSome Areas of Recent Research
University of Chicago Department Retreat, October 2012 Funders & Collaborators NSF (STATMOS), US Department of Energy Faculty: Mihai Anitescu, Liz Moyer Postdocs: Jie Chen, Bill Leeds, Ying Sun Grad students:
More informationHB Methods for Combining Estimates from Multiple Surveys
Hierarchical Bayesian Methods for Combining Estimates from Multiple Surveys Adrijo Chakraborty NORC at the University of Chicago January 30, 2015 Joint work with Gauri Sankar Datta and Yang Cheng Outline
More information